R-Ladies Melbourne Tidy Tuesday showcase

Below you can find some of the code created during the #TidyTuesday night by the R-Ladies Melbourne members across all level or R experience!

Momeneh (Sepideh) Foroutan #TidyTuesday

Measles is a very contagious respiratory infection, and this tidy tuesday data has vaccination rates for 46,412 schools in 32 states in the US, inluding overall vaccination rate, Measles, Mumps, and Rubella (MMR) vaccination rate, as well percent of student who were exempted form the vaccination due to different reasons.

First we read in the data, and load the tidyverse library.

measles <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-25/measles.csv')

library(tidyverse)

Explore the data

Looking at the dimension, head, structure and summary of the data, gives us some insight about the datasets. Bases on these, we realise that there are -1 in the mmr and overall columns, which we may want to take this into account when visualising the data. You may also have noticed that one of the exemption columns is logical instead of numeric. This also help to see which columns have how many NAs.

dim(measles)

## [1] 66113    16

head(measles)

## # A tibble: 6 x 16
##   index state year  name  type  city  county district enroll   mmr overall xrel 
##   <dbl> <chr> <chr> <chr> <chr> <chr> <chr>  <lgl>     <dbl> <dbl>   <dbl> <lgl>
## 1     1 Ariz… 2018… A J … Publ… Noga… Santa… NA           51   100      -1 NA   
## 2     2 Ariz… 2018… Acad… Char… Tucs… Pima   NA           22   100      -1 NA   
## 3     3 Ariz… 2018… Acad… Char… Tucs… Pima   NA           85   100      -1 NA   
## 4     4 Ariz… 2018… Acad… Char… Phoe… Maric… NA           60   100      -1 NA   
## 5     5 Ariz… 2018… Accl… Char… Phoe… Maric… NA           43   100      -1 NA   
## 6     6 Ariz… 2018… Alfr… Publ… Phoe… Maric… NA           36   100      -1 NA   
## # … with 4 more variables: xmed <dbl>, xper <dbl>, lat <dbl>, lng <dbl>

str(measles)

## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 66113 obs. of  16 variables:
##  $ index   : num  1 2 3 4 5 6 7 8 9 10 ...
##  $ state   : chr  "Arizona" "Arizona" "Arizona" "Arizona" ...
##  $ year    : chr  "2018-19" "2018-19" "2018-19" "2018-19" ...
##  $ name    : chr  "A J Mitchell Elementary" "Academy Del Sol" "Academy Del Sol - Hope" "Academy Of Mathematics And Science South" ...
##  $ type    : chr  "Public" "Charter" "Charter" "Charter" ...
##  $ city    : chr  "Nogales" "Tucson" "Tucson" "Phoenix" ...
##  $ county  : chr  "Santa Cruz" "Pima" "Pima" "Maricopa" ...
##  $ district: logi  NA NA NA NA NA NA ...
##  $ enroll  : num  51 22 85 60 43 36 24 22 26 78 ...
##  $ mmr     : num  100 100 100 100 100 100 100 100 100 100 ...
##  $ overall : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ xrel    : logi  NA NA NA NA NA NA ...
##  $ xmed    : num  NA NA NA NA 2.33 NA NA NA NA NA ...
##  $ xper    : num  NA NA NA NA 2.33 NA 4.17 NA NA NA ...
##  $ lat     : num  31.3 32.2 32.1 33.5 33.5 ...
##  $ lng     : num  -111 -111 -111 -112 -112 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   index = col_double(),
##   ..   state = col_character(),
##   ..   year = col_character(),
##   ..   name = col_character(),
##   ..   type = col_character(),
##   ..   city = col_character(),
##   ..   county = col_character(),
##   ..   district = col_logical(),
##   ..   enroll = col_double(),
##   ..   mmr = col_double(),
##   ..   overall = col_double(),
##   ..   xrel = col_logical(),
##   ..   xmed = col_double(),
##   ..   xper = col_double(),
##   ..   lat = col_double(),
##   ..   lng = col_double()
##   .. )

summary(measles)

##      index         state               year               name          
##  Min.   :   1   Length:66113       Length:66113       Length:66113      
##  1st Qu.: 429   Class :character   Class :character   Class :character  
##  Median : 997   Mode  :character   Mode  :character   Mode  :character  
##  Mean   :1608                                                           
##  3rd Qu.:2133                                                           
##  Max.   :8066                                                           
##                                                                         
##      type               city              county          district      
##  Length:66113       Length:66113       Length:66113       Mode:logical  
##  Class :character   Class :character   Class :character   NA's:66113    
##  Mode  :character   Mode  :character   Mode  :character                 
##                                                                         
##                                                                         
##                                                                         
##                                                                         
##      enroll            mmr            overall         xrel        
##  Min.   :   0.0   Min.   : -1.00   Min.   : -1.00   Mode:logical  
##  1st Qu.:  46.0   1st Qu.: -1.00   1st Qu.: -1.00   TRUE:109      
##  Median :  80.0   Median : 95.00   Median : 87.00   NA's:66004    
##  Mean   : 131.9   Mean   : 63.17   Mean   : 54.09                 
##  3rd Qu.: 129.0   3rd Qu.: 98.00   3rd Qu.: 96.10                 
##  Max.   :6222.0   Max.   :100.00   Max.   :100.00                 
##  NA's   :16260                                                    
##       xmed             xper             lat             lng         
##  Min.   :  0.04   Min.   :  0.17   Min.   :24.55   Min.   :-124.50  
##  1st Qu.:  1.00   1st Qu.:  2.84   1st Qu.:35.69   1st Qu.:-117.63  
##  Median :  2.00   Median :  5.00   Median :40.21   Median : -89.97  
##  Mean   :  2.91   Mean   :  6.78   Mean   :39.15   Mean   : -96.28  
##  3rd Qu.:  3.53   3rd Qu.:  7.55   3rd Qu.:42.18   3rd Qu.: -81.75  
##  Max.   :100.00   Max.   :169.23   Max.   :49.00   Max.   :  80.21  
##  NA's   :45122    NA's   :57560    NA's   :1549    NA's   :1549

We can also look at the tables of different columns. Looking at this, we see that the year column has some grouping issues, that you may want to change.

selCols <- c("state", "year", "type")
apply(measles[, selCols], 2, table )

## $state
## 
##        Arizona       Arkansas     California       Colorado    Connecticut 
##           1451            567          16098           1509            795 
##        Florida          Idaho       Illinois           Iowa          Maine 
##           2678            475           7686           1370            357 
##  Massachusetts       Michigan      Minnesota       Missouri        Montana 
##           1594           2351           1813            748            645 
##     New Jersey       New York North Carolina   North Dakota           Ohio 
##           2211           4275           2085            387           3165 
##       Oklahoma         Oregon   Pennsylvania   Rhode Island   South Dakota 
##           1249            817           1939            230            390 
##      Tennessee          Texas           Utah        Vermont       Virginia 
##           1152            811            604            349           1468 
##     Washington      Wisconsin 
##           2221           2623 
## 
## $year
## 
##    2017 2017-18 2018-19    null 
##    1939   10418   48075    5681 
## 
## $type
## 
##        BOCES      Charter Kindergarten    Nonpublic      Private       Public 
##           47          276         1488          173         6815        20692

Visualisation

Now, to do some visualisations in the below steps, we calculate the average of the vaccination rates and different expemtion columns and generate new columns storing these values. Note that we use group_by() and summarise() functions to calculate mean values for each state for these variables; this would drop rows after mean calculations on each group.

Barplot

In order to make barplots for the vaccination rate columns, we make sure that we filter out -1 values from that column. Note that we can order the barplots, simply by using the reorder() function.

measles %>%
  group_by(state) %>%
  summarise(ave_overall = mean(overall, na.rm = T)) %>% 
  filter(ave_overall != -1) %>% 
ggplot(., aes(x = reorder(state, ave_overall), y = ave_overall)) + 
  geom_bar(stat = "identity", fill = "gray20") +
  ggtitle("Average overall vaccination across states") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  

measles %>%
  group_by(state) %>%
  summarise(ave_mmr = mean(overall, na.rm = T)) %>% 
  filter(ave_mmr != -1) %>% 
ggplot(., aes(x = reorder(state, ave_mmr), y = ave_mmr)) + 
  geom_bar(stat = "identity", fill = "gray20") +
  ggtitle("Average MMR vaccination across states") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  

Now, if we want to know what percent exemptions we have in each state, we first calculate the average of percent exemptions and then, change the structure of the data to be in the long format; this puts all the info for different exemptions into one column. To do this this, we use pivot_longer() function. The output of this can be directly used in the ggplot() function. Note that we also filter for NaN valuse in the new column “Average_values”.

measles %>%
  group_by(state) %>%
  mutate(
    ave_xmed = mean(xmed, na.rm = T),
    ave_xper = mean(xper, na.rm = T),
    ave_xrel = mean(xrel, na.rm = T)
  ) %>%
  pivot_longer(.,
               cols = ave_xmed:ave_xrel,
               names_to = "Exemption",
               values_to = "Average_values") %>%
  filter(!is.nan(Average_values)) %>%
  ggplot(., aes(
    x = state,
    y = Average_values,
    fill = Exemption
  )) +
  geom_bar(stat = "identity",
           position = "dodge",
           width = 0.8) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Scatterplot

The other way of looking at the data is to examine associations. For example, there seems to be a positive correlation between overall vaccination rate and mmr, however, there are schools in some states, such as Colorado that have high mmr but generally lower overall vaccination rate.

measles %>%
  filter(overall != -1 & mmr != -1) %>%
  ggplot(., aes(x = mmr, y = overall, color = state)) +
  geom_point(alpha = 0.6) +
  theme_bw()

Map

We can also examine this data in form of a map. There are several ways of doing this but here I am giving two simple examples to get started. These include using the coord_quickmap() function from ggplot2 and plot_usmap() function from usmap package.

coord_quickmap

Here, we plot all lng and lat, and then because there seem to be some outliers in these data, we focus on only those lng and lat that properly covers the US coordinates. Then we show how to simply color that based on the state column.

ggplot(measles, aes(lng, lat)) + 
  geom_point(size = .25, show.legend = FALSE) +
  coord_quickmap()

measles %>% 
  filter(lng < -60 ) %>% 
ggplot(., aes(lng, lat)) + 
  geom_point(size = .25, show.legend = FALSE) +
  coord_quickmap()

measles %>% 
  filter(lng < -60 ) %>% 
ggplot(., aes(lng, lat, color = state)) + 
  geom_point(size = .25, show.legend = FALSE) +
  coord_quickmap()

We can also colour the points based on some continuous values, such as mmr or overall. To have a better contrast in the colour, I decided to keep -1 values, however, you can remove them (by uncommenting the commonted line) and see how your plots looks like.

measles %>%
  filter(lng < -60) %>%
  # filter(mmr != -1) %>%
  ggplot(., aes(lng, lat, color = mmr)) +
  geom_point(size = .25, show.legend = TRUE) +
  scale_color_viridis_c(name = "MMR", label = scales::comma) +
  coord_quickmap() +
  theme(legend.position = "right") +
  theme_dark()

measles %>%
  filter(lng < -60) %>%
  # filter(overall != -1) %>%
  ggplot(., aes(lng, lat, color = overall)) +
  geom_point(size = .25, show.legend = TRUE) +
  scale_color_viridis_c(name = "Overall", label = scales::comma) +
  coord_quickmap() +
  theme(legend.position = "right") +
  theme_dark()

Make it interactive

Now, we subset the data to those that do not have -1 in overall column and color based on the mmr values. We can zoom on different states and make the plots inteactive using the plotly package and ggplotly() function. Uncomment plotly::ggplotly(p, tiptools = "text") in the below code in your script to see the interactivity.

p <- measles %>%
  filter(lng < -110) %>%
  filter(overall != -1) %>%
  ggplot(., aes(lng, lat, color = mmr, text = name)) +
  geom_point(size = .25, show.legend = TRUE) +
  scale_color_viridis_c(name = "MMR", label = scales::comma) +
  coord_quickmap() +
  theme(legend.position = "right") +
  theme_dark()

p

# plotly::ggplotly(p, tiptools = "text")

usmap

As I mentioned above, we can also use usmap package to generate maps for the US. To do this, I have been looking at the post here; but there are obviously other relevant posts, such as this that shows how to generate maps using ggplot2, for example using geom_polygon() function.

Note that as we do not have state abbreviations, we use state.name and state.abb from the usmap, merge these to our data and use plot_usmap() to visualise it.

library(usmap)

abbr_state <- tibble(state = state.name, abb = state.abb)

measles %>%
  group_by(state) %>%
  summarise(ave_mmr = mean(mmr, na.rm = T)) %>%
  filter(ave_mmr != -1) %>%
  left_join(abbr_state) %>%
  plot_usmap(
    data = .,
    region = "state",
    values = "ave_mmr",
    color = "purple"
  ) +
  scale_fill_viridis_c(name = "MMR", label = scales::comma) +
  theme(legend.position = "right") +
  theme(panel.background = element_rect(color = "white", fill = "gray10"))

measles %>%
  group_by(state) %>%
  summarise(ave_overall = mean(overall, na.rm = T)) %>%
  filter(ave_overall != -1) %>%
  left_join(abbr_state) %>%
  plot_usmap(
    data = .,
    region = "state",
    values = "ave_overall",
    color = "purple"
  ) +
  scale_fill_viridis_c(name = "Overall", label = scales::comma) +
  theme(legend.position = "right") +
  theme(panel.background = element_rect(color = "white", fill = "gray10"))

Session info

sessionInfo()

## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
## 
## Matrix products: default
## BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] usmap_0.5.0        forcats_0.4.0      stringr_1.4.0      dplyr_0.8.4       
##  [5] purrr_0.3.3        readr_1.3.1        tidyr_1.0.2        tibble_2.1.3      
##  [9] ggplot2_3.3.0.9000 tidyverse_1.3.0   
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.0.0  xfun_0.12         haven_2.2.0       lattice_0.20-40  
##  [5] colorspace_1.4-1  vctrs_0.2.3       generics_0.0.2    viridisLite_0.3.0
##  [9] htmltools_0.4.0   yaml_2.2.1        utf8_1.1.4        rlang_0.4.4      
## [13] pillar_1.4.3      glue_1.3.1        withr_2.1.2       DBI_1.1.0        
## [17] dbplyr_1.4.2      modelr_0.1.6      readxl_1.3.1      lifecycle_0.1.0  
## [21] munsell_0.5.0     gtable_0.3.0      cellranger_1.1.0  rvest_0.3.5      
## [25] evaluate_0.14     labeling_0.3      knitr_1.28        curl_4.3         
## [29] fansi_0.4.1       broom_0.5.5       Rcpp_1.0.3        backports_1.1.5  
## [33] scales_1.1.0      jsonlite_1.6.1    farver_2.0.3      fs_1.3.1         
## [37] hms_0.5.3         digest_0.6.25     stringi_1.4.6     grid_3.6.2       
## [41] cli_2.0.2         tools_3.6.2       magrittr_1.5      crayon_1.3.4     
## [45] pkgconfig_2.0.3   xml2_1.2.2        reprex_0.3.0      lubridate_1.7.4  
## [49] assertthat_0.2.1  rmarkdown_2.1     httr_1.4.1        rstudioapi_0.11  
## [53] R6_2.4.1          nlme_3.1-144      compiler_3.6.2

Some shots from the night

Many different ways to visualise the #TidyTuesday measles vaccination dataset! #rstats @S_Foroutan pic.twitter.com/AIKXAFuwpx

— R-Ladies Melbourne (@RLadiesMelb) February 27, 2020

The usmap #rstats package has some useful data and functions for plotting US data. Combine with #plotly for interactive graphics.#TidyTuesday @S_Foroutan pic.twitter.com/ER5sLBdD3H

— R-Ladies Melbourne (@RLadiesMelb) February 27, 2020

Nyssa Silbiger #TidyTuesday

if (!require('tidyverse')) install.packages('tidyverse')
if (!require('geofacet')) install.packages('geofacet')
if (!require('ggtext')) install.packages('ggtext')
if (!require('hrbrthemes')) install.packages('hrbrthemes')
if (!require('extrafont')) install.packages('extrafont')

#load libraries
library(tidyverse)
library(geofacet)
library(ggtext)
library(hrbrthemes)
library(extrafont)
library(skimr)

help(package = "tidyverse")

# Download the data
measles <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-25/measles.csv')

d <- read.csv(extrafont:::fonttable_file(), stringsAsFactors = FALSE)
d[grepl("Light", d$FontName), ]$FamilyName <- font_rc_light  # "Roboto Condensed Light"
write.csv(d, extrafont:::fonttable_file(), row.names = FALSE)

extrafont::loadfonts()

skimr::skim(measles)

Data summary

Name	measles
Number of rows	66113
Number of columns	16
_______________________
Column type frequency:
character	6
logical	2
numeric	8
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
state	0	1.00	4	14	0	32	0
year	0	1.00	4	7	0	4	0
name	0	1.00	1	91	0	36129	0
type	36622	0.45	5	12	0	6	0
city	20071	0.70	2	43	0	5665	0
county	6254	0.91	3	21	0	1158	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
district	66113	0	NaN	:
xrel	66004	0	1	TRU: 109

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
index	0	1.00	1607.69	1706.77	1.00	429.00	997.00	2133.00	8066.00	▇▂▁▁▁
enroll	16260	0.75	131.93	162.16	0.00	46.00	80.00	129.00	6222.00	▇▁▁▁▁
mmr	0	1.00	63.17	45.72	-1.00	-1.00	95.00	98.00	100.00	▅▁▁▁▇
overall	0	1.00	54.09	46.67	-1.00	-1.00	87.00	96.10	100.00	▆▁▁▁▇
xmed	45122	0.32	2.91	3.85	0.04	1.00	2.00	3.53	100.00	▇▁▁▁▁
xper	57560	0.13	6.78	7.62	0.17	2.84	5.00	7.55	169.23	▇▁▁▁▁
lat	1549	0.98	39.15	4.58	24.55	35.69	40.21	42.18	49.00	▁▃▅▇▂
lng	1549	0.98	-96.28	18.44	-124.50	-117.63	-89.97	-81.75	80.21	▇▃▁▁▁

summary(measles)

##      index         state               year               name          
##  Min.   :   1   Length:66113       Length:66113       Length:66113      
##  1st Qu.: 429   Class :character   Class :character   Class :character  
##  Median : 997   Mode  :character   Mode  :character   Mode  :character  
##  Mean   :1608                                                           
##  3rd Qu.:2133                                                           
##  Max.   :8066                                                           
##                                                                         
##      type               city              county          district      
##  Length:66113       Length:66113       Length:66113       Mode:logical  
##  Class :character   Class :character   Class :character   NA's:66113    
##  Mode  :character   Mode  :character   Mode  :character                 
##                                                                         
##                                                                         
##                                                                         
##                                                                         
##      enroll            mmr            overall         xrel        
##  Min.   :   0.0   Min.   : -1.00   Min.   : -1.00   Mode:logical  
##  1st Qu.:  46.0   1st Qu.: -1.00   1st Qu.: -1.00   TRUE:109      
##  Median :  80.0   Median : 95.00   Median : 87.00   NA's:66004    
##  Mean   : 131.9   Mean   : 63.17   Mean   : 54.09                 
##  3rd Qu.: 129.0   3rd Qu.: 98.00   3rd Qu.: 96.10                 
##  Max.   :6222.0   Max.   :100.00   Max.   :100.00                 
##  NA's   :16260                                                    
##       xmed             xper             lat             lng         
##  Min.   :  0.04   Min.   :  0.17   Min.   :24.55   Min.   :-124.50  
##  1st Qu.:  1.00   1st Qu.:  2.84   1st Qu.:35.69   1st Qu.:-117.63  
##  Median :  2.00   Median :  5.00   Median :40.21   Median : -89.97  
##  Mean   :  2.91   Mean   :  6.78   Mean   :39.15   Mean   : -96.28  
##  3rd Qu.:  3.53   3rd Qu.:  7.55   3rd Qu.:42.18   3rd Qu.: -81.75  
##  Max.   :100.00   Max.   :169.23   Max.   :49.00   Max.   :  80.21  
##  NA's   :45122    NA's   :57560    NA's   :1549    NA's   :1549

## get percentage of students that did not get the mmr shot for religious, medical, or personal reasons accross all states
measles_df <- measles %>%
  filter(mmr >0) %>% #removed schools with no reported value (-1)
  mutate(xrel = as.numeric(xrel))%>% # it is showing up as a logical instead of a value
  replace_na(list(xmed = 0, xrel = 0, xper = 0, enroll = 0, overall = 0, mmr = 0)) %>% # replace all NA with 0
  dplyr::select(-lat, -lng, -index, -enroll, -overall) %>% #remove lat and long
  pivot_longer(cols = xrel:xper, names_to = "Reason", values_to = "percentage") %>% # have all the reasons together for the barplot below
  group_by(state, Reason) %>%
  summarise_if(is.numeric, list(~mean(.), ~sd(.)/sqrt(n()))) %>% # get means and SE
  rename(SE = `percentage_/`)

glimpse(measles_df)

## Observations: 63
## Variables: 6
## Groups: state [21]
## $ state           <chr> "Arizona", "Arizona", "Arizona", "Arkansas", "Arkansa…
## $ Reason          <chr> "xmed", "xper", "xrel", "xmed", "xper", "xrel", "xmed…
## $ mmr_mean        <dbl> 92.63085, 92.63085, 92.63085, 80.49296, 80.49296, 80.…
## $ percentage_mean <dbl> 0.28127242, 6.14345858, 0.00000000, 0.00000000, 0.000…
## $ `mmr_/`         <dbl> 0.26844577, 0.26844577, 0.26844577, 0.36855464, 0.368…
## $ SE              <dbl> 0.0253427950, 0.2329142840, 0.0000000000, 0.000000000…

# rename the SE column
ggplot(measles_df,aes(x = Reason, y = percentage_mean, color = Reason, fill = Reason))+
  geom_bar(stat = 'identity')+
  geom_errorbar(aes(ymin = percentage_mean - SE, ymax = percentage_mean+SE), color = "white", width = 0)+
 # theme_ft_rc() + # black background theme
  labs(caption = "Plot by N. Silbiger \n@nsilbiger \nData by the Wallstreet Journal")+
  theme(axis.title.x=element_blank(),  # remove xlabels
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        #plot.title = element_markdown(lineheight = 1.1)
        
        legend.position = "none")+
  xlab("")+ylab("")+
  labs(title = "Mean prcentage of students that refused vaccines due to  medical , personal , or  religious")+
  facet_geo(~ state) # facet wrap it by state

measles_df2 <- measles %>%
  filter(mmr >0) %>% #removed schools with no reported value (-1)
  mutate(xrel = as.numeric(xrel))%>% # it is showing up as a logical instead of a value
  replace_na(list(xmed = 0, xrel = 0, xper = 0, enroll = 0, overall = 0, mmr = 0)) # replace all NA with 0
glimpse(measles_df2)

## Observations: 44,157
## Variables: 16
## $ index    <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 11, 12, 13, 14, 15, 15, 1…
## $ state    <chr> "Arizona", "Arizona", "Arizona", "Arizona", "Arizona", "Ariz…
## $ year     <chr> "2018-19", "2018-19", "2018-19", "2018-19", "2018-19", "2018…
## $ name     <chr> "A J Mitchell Elementary", "Academy Del Sol", "Academy Del S…
## $ type     <chr> "Public", "Charter", "Charter", "Charter", "Charter", "Publi…
## $ city     <chr> "Nogales", "Tucson", "Tucson", "Phoenix", "Phoenix", "Phoeni…
## $ county   <chr> "Santa Cruz", "Pima", "Pima", "Maricopa", "Maricopa", "Maric…
## $ district <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ enroll   <dbl> 51, 22, 85, 60, 43, 36, 24, 22, 26, 78, 78, 35, 54, 54, 34, …
## $ mmr      <dbl> 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, …
## $ overall  <dbl> -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, …
## $ xrel     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ xmed     <dbl> 0.00, 0.00, 0.00, 0.00, 2.33, 0.00, 0.00, 0.00, 0.00, 0.00, …
## $ xper     <dbl> 0.00, 0.00, 0.00, 0.00, 2.33, 0.00, 4.17, 0.00, 0.00, 0.00, …
## $ lat      <dbl> 31.34782, 32.22192, 32.13049, 33.48545, 33.49562, 33.43532, …
## $ lng      <dbl> -110.9380, -110.8961, -111.1170, -112.1306, -112.2247, -112.…

ggplot(measles_df2,aes(x = enroll, y = mmr, colour = state, fill = state))+
  geom_point( )

measles_df3 <- measles %>%
  filter(mmr >0) %>% #removed schools with no reported value (-1)
  mutate(xrel = as.numeric(xrel))%>% # it is showing up as a logical instead of a value
  replace_na(list(xmed = 0, xrel = 0, xper = 0, enroll = 0, overall = 0, mmr = 0)) %>%# replace all NA with 0
  group_by(type,state) %>% # group for visualisation
  summarise(enrollments = sum(enroll), avg_mmr = mean(mmr) ) %>%
  filter(type %in% list('Private','Public'))

glimpse(measles_df3)

## Observations: 14
## Variables: 4
## Groups: type [2]
## $ type        <chr> "Private", "Private", "Private", "Private", "Private", "P…
## $ state       <chr> "Arizona", "California", "Colorado", "Massachusetts", "Ne…
## $ enrollments <dbl> 2943, 60612, 2680, 0, 0, 24309, 4819, 66548, 1172146, 0, …
## $ avg_mmr     <dbl> 89.14419, 93.87482, 95.12190, 93.88889, 93.62700, 91.9075…

ggplot(measles_df3,aes(x = enrollments, y = avg_mmr, colour = state, fill = state, label=state))+
  geom_point( ) +
  geom_text(angle = 45)

ggplot(measles_df3,aes(x = type, y = avg_mmr, colour = state, fill = state, label=state))+
  geom_bar(position = "dodge", stat="identity" ) #+

#    geom_text(angle = 45)

Eka Tian #TidyTuesday

# By Eka Tian
# 28/Feb/2020
if (!require('tidyverse')) install.packages('tidyverse')
if (!require('geofacet')) install.packages('geofacet')
if (!require('ggtext')) install.packages('ggtext')
if (!require('hrbrthemes')) install.packages('hrbrthemes') 
if (!require('extrafont')) install.packages('extrafont')
devtools::install_github("wilkelab/ggtext")

#load libraries
library(tidyverse)
library(geofacet)
library(hrbrthemes)
library(extrafont)
library(skimr)
library(dplyr)
library(ggtext)

# Download the data
measles <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-25/measles.csv')

measles_df3 <- measles %>%
  filter(mmr >0) %>% #removed schools with no reported value (-1)
  mutate(xrel = as.numeric(xrel))%>% # it is showing up as a logical instead of a value
  replace_na(list(xmed = 0, xrel = 0, xper = 0, enroll = 0, overall = 0, mmr = 0)) %>%# replace all NA with 0
  group_by(type,state) %>% # group for visualisation
  summarise(enrollments = sum(enroll), avg_mmr = mean(mmr) ) %>%
  filter(type %in% list('Private','Public'))

glimpse(measles_df3)

## Observations: 14
## Variables: 4
## Groups: type [2]
## $ type        <chr> "Private", "Private", "Private", "Private", "Private", "P…
## $ state       <chr> "Arizona", "California", "Colorado", "Massachusetts", "Ne…
## $ enrollments <dbl> 2943, 60612, 2680, 0, 0, 24309, 4819, 66548, 1172146, 0, …
## $ avg_mmr     <dbl> 89.14419, 93.87482, 95.12190, 93.88889, 93.62700, 91.9075…

ggplot(measles_df3,aes(x = type, y = avg_mmr, colour = state, fill = state, label=state))+
  geom_bar(position = "dodge", stat="identity" ) 

More insights from the night!

Getting into the measles vaccination data at our #TidyTuesday working session! pic.twitter.com/aolod0xTRb

— R-Ladies Melbourne (@RLadiesMelb) February 27, 2020

Average measles vaccination rate is highest for Arizona and New York #TidyTuesday pic.twitter.com/Cwpt1RBqDD

— R-Ladies Melbourne (@RLadiesMelb) February 27, 2020

Exemptions from measles vaccinations for medicine, personal or religion reasons drop off with the size of the school#TidyTuesday pic.twitter.com/0x6EXkQIeV

— R-Ladies Melbourne (@RLadiesMelb) February 27, 2020

Public sector has higher vaccination rates #TidyTuesday
Are the wealthy more likely to opt out of vaccinations? 🤔 pic.twitter.com/cj1V4c7ylB

— R-Ladies Melbourne (@RLadiesMelb) February 27, 2020

California has the highest rate of MMR vaccinations. Insights from #TidyTuesday data pic.twitter.com/GaYVfQ2XFB

— R-Ladies Melbourne (@RLadiesMelb) February 27, 2020

Interested in rates of measles, mumps and rubella vaccinations? We're learning all about them with the #TidyTuesday dataset@annaquagli pic.twitter.com/IQSLlbfMqq

— R-Ladies Melbourne (@RLadiesMelb) February 27, 2020